The purpose of this markdown is to describe the differences of data
from the World Tuna Atlas following the different filters. It describes
the differences between the initial data and the final data, and is
intended for users of the final data who might be tempted to use it
without taking into account the various filtering specificities.
Attention ! All the differences inferior to 0 corresponds to gain
in captures.
The data compared
The analyzed data are :
There are no filter on this data
The filter used on this data are:
- on species:
- on gears:
- on rfmos:
- on fleets:
- on initial dates:
- on final dates:
- on geographical categories:
- on catch types:
There are no differences between the options to create the two
datasets.They are created by the same workflow.
The options used to create the final data frame are:
Options | Value |
include-IOTC | TRUE, |
include-ICCAT | TRUE, |
include-IATTC | TRUE, |
include-WCPFC | TRUE, |
include-CCSBT | TRUE, |
iattc-ps-catch-billfish-shark-raise-to-effort | TRUE, |
iattc-ps-raise-flags-to-schooltype | TRUE, |
iattc-ps-dimension-to-use-if-no-raising-flags-to-schooltype | NULL, |
iccat-ps-include-type-of-school | TRUE, |
NONEiattc-ps-dimension-to-use-if-no-raising-flags-to-schooltype | NULL, |
Main differences
The number of lines goes from 4.679497 millions in
rawdata_modyfing_georeferenced_errors to 4.703429 millions in
treatment_after_binding, which correspond to a difference of
-0.51142%.
The initial dataset has a total of 103363606 in tons and of
1148126339 in number of fish.
The final dataset has a total of 103386195 in tons and of 1148126339
in number of fish.
The differences is -22589 in tons (-0.02%). The
differences is 0 in number of fish (0).
The stratas differences between the first one and the second one are
:
(only first 10 per Dimension showed), representing 0 % of the total
number of stratas.
Dimension | unit | Loss / Gain | Precision | Captures table 1 | Captures table 2 | Difference (in %) | Difference in millions of tons/fish |
fishingfleet |
|
|
|
|
|
|
|
| Tons |
|
|
|
|
|
|
|
| Gain |
|
|
|
|
|
|
|
| UNK | 0 | 2,995.423 | 100 | -0.002995423 |
schooltype |
|
|
|
|
|
|
|
|
|
| fs | 0 | 2,677,891.474 | 100 | -2.677891474 |
|
|
| fd | 0 | 4,130,594.010 | 100 | -4.130594010 |
Introduction to the two
datasets
We first present the main characteristics of each dimension for each
dataset.
Comparison of the two
catch evolutions
For each dataset, we compare the catch evolution and the cumulative
catch evolution both in tons and number of fish.

Comparison of the
evolution of the cumulative catches


Spatial coverage
We represent the spatial coverage, faceted by the geographical
category. The geographical category depends on the area of the
geographic polygon. In this case there are categories which are . The
category is made on the area, also different shapes could be included in
the same geographical category (example 5x10, 10x5 and 25x2).
Spatial coverage in
number of fish
Differences
This section detail the different differences that observed between
the dataframe rawdata_modyfing_georeferenced_errors and
treatment_after_binding.
The differences for
each dimension
We will look for each dimension the 6 most important differences
Dimension | unit | Loss / Gain | Precision | Captures table 1 | Captures table 2 | Difference (in %) | Difference in millions of tons/fish |
cat_geo |
|
|
|
|
|
|
|
| Tons |
|
|
|
|
|
|
|
| Gain |
|
|
|
|
|
|
|
| 1_deg | 28,467,958 | 28,490,547.41 | -0.08 | -0.02 |
fishingfleet |
|
|
|
|
|
|
|
|
|
| KOR | 779,384 | 779,384.16 | 0.00 | 0.00 |
|
|
| CHN | 617,752 | 617,752.33 | 0.00 | 0.00 |
|
|
| UNK | 0 | 2,995.42 | -Inf | 0.00 |
|
|
| GHA | 2,646,121 | 2,671,496.34 | -0.96 | -0.03 |
|
| Loss |
|
|
|
|
|
|
|
| VEN | 560,794 | 556,797.65 | 0.71 | 0.00 |
|
|
| CPV | 304,208 | 303,484.95 | 0.24 | 0.00 |
|
|
| PAN | 500,784 | 499,722.52 | 0.21 | 0.00 |
species |
|
|
|
|
|
|
|
|
| Gain |
|
|
|
|
|
|
|
| ALB | 6,365,656 | 6,365,904.40 | 0.00 | 0.00 |
|
|
| YFT | 24,643,766 | 24,646,270.52 | -0.01 | 0.00 |
|
|
| BET | 9,253,016 | 9,257,795.22 | -0.05 | 0.00 |
|
|
| FRI | 203,693 | 204,155.40 | -0.23 | 0.00 |
|
|
| SKJ | 51,557,987 | 51,572,587.66 | -0.03 | -0.01 |
|
| Loss |
|
|
|
|
|
|
|
| BLF | 15,791 | 15,785.84 | 0.03 | 0.00 |
gear |
|
|
|
|
|
|
|
|
| Gain |
|
|
|
|
|
|
|
| PS | 18,755,581 | 18,778,912.02 | -0.12 | -0.02 |
|
| Loss |
|
|
|
|
|
|
|
| BB | 6,457,969 | 6,457,226.96 | 0.01 | 0.00 |
|
|
| LL | 7,496,739 | 7,496,738.57 | 0.00 | 0.00 |
species_group |
|
|
|
|
|
|
|
|
| Gain |
|
|
|
|
|
|
|
| Temperate tunas | 7,419,031 | 7,419,279.69 | 0.00 | 0.00 |
|
|
| Neritic tunas | 1,186,167 | 1,186,628.61 | -0.04 | 0.00 |
|
|
| Tropical tunas | 85,470,613 | 85,492,491.44 | -0.03 | -0.02 |
source_authority |
|
|
|
|
|
|
|
|
|
| ICCAT | 20,222,700 | 20,245,289.50 | -0.11 | -0.02 |
Name_En |
|
|
|
|
|
|
|
|
|
| NA | 103,363,606 | 103,386,195.40 | -0.02 | -0.02 |
Differences in
geographical data
Here is represented for each area the polygons keeping all the
initial information, the one losing a part and the one losing all the
information. The polygongs with red bord lost all the information, the
one with blue borders did not loose information.
The coverage difference is -1 km square.
Differences in
temporal data
Here is represented the differences in percent for each year.
